Over the last two decades, manufacturing across the globe has evolved to be more intel-ligent and data driven. In the age of industrial Internet of Things, a smart production unit can be perceived as a large connected industrial system of materials, parts, machines, tools, inventory, and logistics that can relay data and communicate with each other. While, traditionally, the focus has been on machine health and predictive maintenance, the manufacturing industry has also started focusing on analyzing data from the entire production line. These applications bring a new set of analytics challenges. Unlike tradi-tional data mining analysis, which consists of lean datasets (that is, datasets with few fea-tures), manufacturing has fat datasets. In addition, previous approaches to manufacturing analytics restricted themselves to small time periods of data. The latest advances in big data analytics allows researchers to do a deep dive into years of data. Bosch collects and utilizes all available information about its products to increase its understanding of complex linear and nonlinear relationships between parts, machines, and assembly lines. This helps in use cases such as the discovery of the root cause of internal defects. This article presents a case study and provides detail about challenges and approaches in data extraction, modeling, and visualization.

Manufacturing Analytics and Industrial Internet of Things

The classification in class imbalanced data has drawn significant interest in medical application. Most existing methods are prone to categorize the samples into the majority class, resulting in bias, in particular the insufficient identification of minority class. A kind of novel approach, class weights random forest is introduced to address the problem, by assigning individual weights for each class instead of a single weight. The validation test on UCI data sets demonstrates that for imbalanced medical data, the proposed method enhanced the overall performance of the classifier while producing high accuracy in identifying both majority and minority class.

Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data

Implementing Six Sigma

The purpose of this paper is to illustrate the systematic role played by Six Sigma methodology in improving the quality of healthcare. The literature review identifies the relevant opportunities for successful introduction and development of Six Sigma approach in healthcare sector.,A systematic methodology to identifying literature on Six Sigma in healthcare is presented. Web of Science, Medline, Emerald Insight, ASQ and ProQuest databases (1998-2016) were searched, and 68 papers of fair methodological quality were identified.,The findings of the systematic review reveal a growing interest in research on Six Sigma adoption in healthcare. The findings indicate that Six Sigma applications in healthcare have been focused on the entire hospital with no real focus on a particular department or function. The key findings on benefits, success factors, challenges and common tools of Six Sigma from the existing literature are also presented in the paper.,The papers included in the systematic review were peer-reviewed papers available in English. Due to these limitations, relevant papers may have been excluded. Moreover, the authors have excluded all conference and white papers for their inclusion in this study.,This paper can serve as a guide on how Six Sigma approach can be applied to improve the quality of healthcare. The authors also believe that this is possibly the most comprehensive systematic literature review on the topic and will set the foundation for various research avenues based on the key findings of this study.

Six Sigma in healthcare: a systematic review of the literature

Overview of recent advances of process analysis and quality control in resistance spot welding

Real-time estimation of weld quality from process data is one of the key objectives in current weld control systems for resistance spot-welding processes. This task can be alleviated if the weld controller is equipped with a voltage sensor in the secondary circuit. Replacing the goal of quantifying the weld quality in terms of button size by the more modest objective of indirect estimation of the class of the weld, e.g., satisfactory (acceptable, “normal” button size), unsatisfactory (undersized, “cold” welds), and defects (“expulsion”), further improves the feasibility of the mission of indirect estimation of the weld quality. This paper proposes an algorithmic framework based on a linear vector quantization (LVQ) neural network for estimation of the button size class based on a small number of dynamic resistance patterns for cold, normal, and expulsion welds that are collected during the stabilization process. Nugget quality classification by using an LVQ network was tested on two types of controllers; medium-frequency direct current (MFDC) with constant current controller and alternating current (AC) with constant heat controller. In order to reduce the dimensionality of the input data vector, different sets of features are extracted from the dynamic resistance profile and are compared by using power of the test criteria. The results from all of these investigations are very promising and are reported here in detail.

Online qualitative nugget classification by using a linear vector quantization neural network for resistance spot welding

Feature selection from imbalance data plays an important role in building efficient support decision systems, improving the machine learning process performance and enhancing the classification accuracy. The problem of feature selection becomes even more difficult with imbalance data, which occurs in real-world domains when the classes representing the data set are not equally distributed. Using the traditional classifiers to seek an accurate performance over a full range of instances is not suitable to deal with imbalanced learning tasks, since they tend to classify all the data into one class. In this paper, the Mahalanobis genetic algorithm (MGA) classifier is proposed to address the problem of feature selection for imbalance welding data. The MGA classifier was benchmarked with the Mahalanobis-Taguchi system (MTS) classifier, in terms of the following metrics: the total misclassification errors, the area under the curve (AUC) for receiver operating characteristic (ROC) curves, and the signal-to-noise (S/N) ratio. A real-life data set from the spot welding process was used as a pilot study. The results in terms of the total misclassification error and the AUC metrics showed that the MGA had better classification performance than MTS. Very close results were obtained when the training data set was balanced by using the Synthetic Minority Oversampling Technique (SMOTE) which indicates the suitability of the MGA and MTS classifiers to be used for the imbalance data set without using any preprocessor approach. Regarding the S/N ratio, the results were inconsistent with the other classification metrics, which raises the question about its credibility.

A novel approach for classifying imbalance welding data: Mahalanobis genetic algorithm (MGA)

The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB), Probabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal Transformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes algorithms. MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400. A real life case study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance with Mahalanobis Genetic Algorithm (MGA).

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Modified Mahalanobis Taguchi System for Imbalance Data Classification.

Health care is becoming more expensive and so there is a critical need to increase the efficiency of the health care delivery process.

Patient Discharge Time Improvement by Using the Six Sigma Approach: A Case Study

Resistance spot welding is one of the primary means of joining sheet metal in the automotive industry and other industries. The demand for improved corrosion resistance has led the automotive industry to increasingly use zinc coated steel in auto body construction. One of the major concerns associated with welding coated steel is the mushrooming effect (the increase in the electrode diameter due to deposition of copper into the spot surface) resulting in reduced current density and undersized welds (cold welds). The most common approach to this problem is based on the use of simple unconditional incremental algorithms (steppers) for preprogrammed current scheduling. In this paper, an intelligent algorithm is proposed for adjusting the amount of current to compensate for the electrodes degradation. The algorithm works as a fuzzy logic controller using a set of engineering rules with fuzzy predicates that dynamically adapt the secondary current to the state of the weld process. The state is identified by indirectly estimating two of the main process characteristics - weld quality and expulsion rate. A soft sensor for indirect estimation of the weld quality employing a learning vector quantization (LVQ) type classifier is designed to provide a real time approximate assessment of the weld nugget diameter. Another soft sensing algorithm is applied to predict the impact of changes in current on the expulsion rate of the weld process. By maintaining the expulsion rate just below a minimal acceptable level, robust process control performance and satisfactory weld quality are achieved. The intelligent constant current control for resistance spot welding is implemented and validated on a medium frequency direct current (MFDC) constant current weld controller. Results demonstrate a substantial improvement of weld quality and reduction of process variability due to the proposed new control algorithm.

Mahmoud El-Banna

Papers

Online qualitative nugget classification by using a linear vector quantization neural network for resistance spot welding

A novel approach for classifying imbalance welding data: Mahalanobis genetic algorithm (MGA)

Modified Mahalanobis Taguchi System for Imbalance Data Classification.

Patient Discharge Time Improvement by Using the Six Sigma Approach: A Case Study

Intelligent Constant Current Control for Resistance Spot Welding